What is the impact of
gendered headship on food
and nutrition security in the
breadbasket of Tanzania?
An investigation on cross-sectional
data in rural Tanzania
What is the impact of gendered headship on
food and nutrition security in the breadbasket
of Tanzania?
An investigation on cross-sectional data in rural Tanzania
Lavinia Plataroti
MSc. in Development Studies
WUR - WAGENINGEN UNIVERSITY AND RESEARCH
Development Economics group
22nd April, 2016
Supervisor: Marrit van den Berg
Co-supervisor: Janneke Pieters
i
Abstract
This thesis uses cross-sectional data from 1648 interviews of small-holder household to identify the effect of
gendered headship on food and nutrition security in rural Tanzania. Multivariate Ordinary Least Squares
regressions and Ordered probit model were used to analyse asset endowments, food and non food
consumption behaviour and dietary diversity for male- and female-headed households. Results show that,
gender had significant and strong effect in determining per capita consumption, being assets constant.
However, such an effect was not found when per capita food consumption was analysed. Studying nutritional
livelihood outcomes, gender did not show to be a driver in good nutritional food choices. On the contrary,
the level of per capita consumption, as a proxy for assets accumulation and household income, resulted to
be the real driver of food consumption patterns and nutrition. In conclusion, the advantage of being a femaleheaded households, that was showed in the first model, when assets were analysed, did not compensate for
the lack of assets as showed in the second and third model.
ii
Acknowledgments
Many people have contributed to this work. First of all, I would like to thank all the precious people I met in
Tanzania during the data collection. Enumerators, supervisors, friends and simple acquaintances who all
have inspired, assisted and guided me on the field. I would like to thank Abiud Bongole for his
recommendations and endless talk during the data collection and the analysis.
I would like to say thank you to Romy Appelman, Jasmijn Appels and Siva van Leerzem for the hard work,
the many chats and company during this thesis-work journey.
I would like to thank my supervisor, Marrit van der Berg, for the chance she gave me to visit Tanzania and
her professional and critical support during the data collection and the analysis.
Lastly, I would like to thank my friends and, above all, my family who has always been motivating me to
challenge myself for personal and professional improvements. Thank you for your love.
iii
Table of Contents
Abstract .............................................................................................................................................................. ii
Acknowledgments ............................................................................................................................................ iii
List of Tables...................................................................................................................................................... vi
List of Figures .................................................................................................................................................... vi
1
Introduction ............................................................................................................................................... 1
1.1
2
1.1.1
Gap............................................................................................................................................. 1
1.1.2
Opportunity ............................................................................................................................... 2
1.2
Problem statement ............................................................................................................................ 3
1.3
Research Questions ........................................................................................................................... 4
Theoretical Framework ............................................................................................................................. 5
2.1
Vulnerability context ................................................................................................................. 6
2.1.2
Transforming structures and processes .................................................................................... 6
2.1.3
Livelihood strategies .................................................................................................................. 7
2.1.4
Livelihood outcomes.................................................................................................................. 8
Models ............................................................................................................................................. 14
4.1.1
Model 1: Gender and Households Consumption .................................................................... 14
4.1.2
Model 2: Gender and Food Consumption Pattern .................................................................. 15
4.1.3
Model 3: Gender and Dietary Diversity ................................................................................... 15
4.2
Data Collection and Materials ......................................................................................................... 16
4.3
Variables description ....................................................................................................................... 17
4.3.1
Measuring Food and Nutrition Security .................................................................................. 17
4.3.2
Households demographic characteristics................................................................................ 20
4.3.3
Other households’ characteristics ........................................................................................... 21
4.4
iv
Study area ........................................................................................................................................ 10
Methodology ........................................................................................................................................... 14
4.1
5
Economic theory of household decision ........................................................................................... 9
Background .............................................................................................................................................. 10
3.1
4
Sustainable Livelihood Framework.................................................................................................... 5
2.1.1
2.2
3
Gender: gap or opportunity? ............................................................................................................. 1
Descriptive statistics ........................................................................................................................ 21
Results ..................................................................................................................................................... 25
5.1
Determining the impact of gender on household consumption ..................................................... 25
5.2
Determining the impact of gender on food consumption patterns................................................ 26
5.3
Determining the impact of gender on dietary diversity .................................................................. 30
6
Discussion ................................................................................................................................................ 32
6.1
7
IV approach...................................................................................................................................... 34
Conclusions .............................................................................................................................................. 36
Appendix .......................................................................................................................................................... 37
References list ................................................................................................................................................. 39
v
List of Tables
Table 1 - Food Consumption share - Aggregation into food groups ............................................................... 18
Table 2 - WDDS - Aggregation of food categories ........................................................................................... 19
Table 3 - Descriptive statistics of Variable....................................................................................................... 23
Table 4 - Descriptive statistics of food groups. Means (in %) of consumption shares .................................... 24
Table 5 - Coefficients and Margins of ordered probit model .......................................................................... 31
Table 6 - First stage IV regression output ........................................................................................................ 34
Table 7- Under-identification test and Over-identification test...................................................................... 37
Table 8 - Cragg-Donals Wald F-stat and critical Values ................................................................................... 38
List of Figures
Figure 1 Sustainable Livelihood Framework...................................................................................................... 8
Figure 3 Food Balance Sheets in Tanzania, 2013............................................................................................. 11
Figure 5 - Map of United Republic of Tanzania and Districts .......................................................................... 13
Figure 6 - Distribution of Dietary Diversity Score ............................................................................................ 20
vi
Chapter 1
1 Introduction
According to the latest figures, in 2012-14, about 805 million people were estimated to be chronically
undernourished. (FAO; IFAD and WFP, 2014). Even though improvements have been made in the recent years
in almost all the developing countries, important differences exist (FAO; IFAD and WFP, 2014). A great
number of low-income countries are still poorly improving and Africa remain the region of a major concern.
It has been estimated that, in Sub-Sahara countries, one fourth of the population is hungry. This makes Africa
the continent with the largest prevalence of malnourished people (FAO; IFAD and WFP, 2014).
Since the end of the ‘80s, women have been appointed as the key actor for tackling malnutrition and food
insecurity worldwide (FAO, 2010) (Horrell & Krishnan, 2007). Up to then, a lack of attention on gender aspects
in many development programmes, have contributed to higher level of poverty and undernutrition
(Quisumbing, 2003). Ever since, academics and practitioners in development studies have started to
implement programmes to strength the role of gender factors in various sectors, especially in agriculture and
food (Kabeer, 2003). Concerns about food and nutrition security led to highlight the gender’s role in
household headship.
Although, female-headed household are expected being more likely to be found among the poor, this is hard
to be verified in general, especially when poverty is not solely related to income (Chant, 2003). The World
Bank data showed that, although this might be true for Asia and Latin America, this is less likely the case in
Africa (Chant, 2003).
This work aims at estimating the impacts of gendered household headship on food and nutrition security. In
particular, I will investigate the relation between gendered headship and food consumption as well as
nutritional indicators in the framework of Sustainable Livelihood approach.
1.1 Gender: gap or opportunity?
1.1.1 Gap
A vast literature supports the idea that women and girls face a number of socio-economic constraints in
accessing strategic resources and reaching successful livelihood strategies against poverty and malnutrition
(FAO, 2010) (Ogato, Boon, & Subramani, 2009)(Quisumbing, Haddad, & Peña, 2001) (Hadley, Lindstrom,
Tessema, & Belachew, 2008) (Sraboni, Malapit, Quisumbing, & Ahmed, 2014). Those constraints are widely
known Gender Gap. Women are poorly endowed with production assets so that such a limitation negatively
affects the ability of women to improve their livelihoods. It reduces their food security and limit their wellbeing.
This condition affects not only women as individuals but also women as head of household. Therefore,
female-headed households would be worse off than male-headed households, because women cannot
provide as good livelihoods as male-headed households.
Empirical findings provide plenty of examples in this regards. For instance, (Kassie, Ndiritu, & Stage, 2014),
argues that women may be disadvantaged comparing to men in terms of access to land, fertilizers and credit.
Specifically, studies showed that women face constraints in holding property rights over land and assets.
1
Constraints often rise when it comes to inheritance and in circumstances of death of, or divorce from a
spouse. (A. Ellis, Blackden, Cutura, MacCulloch, & Seebens, 2007), suggest that women, in Tanzania, own
about 19% of registered land and that the average plot size owed by women is less than half the size of those
owned by men.
Along this line of literature, numerous studies found that female farmers might face restrictions in getting
access to extension services and updated improvements in technology, through which a great deal of
innovation are channelled to the farmer level. (Kassie et al., 2014) The consequences for female farmers is
that they are likely to be less competitive when compared with male farmers and, as a result, they might
make smaller income.
Moreover, forms of gender inequalities have been proven to be linked not only in on-farm income generating
activities, but also on off-farm employed jobs. Women are likely to receive lower wages than men are
(Lanjouw, Jean., Lanjouw, 2001). The ILFS (Integrated Labour Force Survey) has shown that in 2007 in most
off-farm jobs in Tanzania, men realize higher earnings compared to women. Both on-farm and off-farm work,
might affect the ability for female-headed household to get access to proper and enough food, which directly
undermine food and nutrition security at the household level.
Some studies provide evidence indicating that women are more likely to be single parent (Ndobo &
Sekhampu, 2013). In rural context, single parents might experience time constraints since time dedicated to
agricultural production put pressure on household’s chores as well as on children’s care.
Additionally, some forms of social perception of women at the community level tend to discredit women’s
capabilities and their suitability as farmers (A. Ellis et al., 2007). This condition might influences resource
allocation at the community level, negatively affecting food security for female-headed households.
1.1.2 Opportunity
At the same time, based on a number of empirical evidence, research have theorized that belonging to a
female headed-household might also liberate resources for women and leading to an improvement in
household food and nutrition security. (Mason, Ndlovu, Parkins, & Luckert, 2014) (Chant, 2007) (Kabeer,
2003). This is the idea behind a great body of researches that support women empowerment as measure of
poverty reduction.
This stream of empirical evidences originates from the analysis of women’s role in the development of rural
household livelihoods. Women’s reproductive function is at the core of household livelihood. Such a function
implies that women and girls in many developing countries usually undertake numerous domestic tasks, such
as processing food crop, collecting water, fetching wood. (A. Ellis et al., 2007). They take care of home
gardens throughout which women assure food consumption for the whole household (Boserup, 1970) (FAO,
2010) (A. Ellis et al., 2007). Food consumption is crucial for survival and time series studies have shown that
women provide 85-90% of the overall time spent for household food preparation (FAO, 2010).
Women are responsible for tendering animals. Although caring style and the type of animal may vary
according to cultural settings, a common pattern shows that women take care of poultry (FAO 1998, Guèye
200, Tung 2005 in (FAO, 2010)), diary animals (Okali and Mins, 1998,Tangka, Jabbar and Sharipo 2000 in (FAO,
2010)) and other animals that are housed and fed at home. Moreover, two third of the poor livestock
keepers, which account for 400 million people, are women (FAO, 2010). Although most of these activities are
not “economically valuable”, they substantially contribute to develop household’s livelihoods and well-being.
Thornton et al, 2002 in (FAO, 2010).
2
In addition, women engage in a number of different forms in agricultural production. Women engage in
agriculture as farmers, (often unpaid) labourer in family farms or workers in someone else’s farm. Activities
that encompass both production and marketing. They are involved in both crop and livestock production.
Their labour is provided for both subsistence and commercial production. (FAO, 2010). FAO estimates that
women comprise 43% of agricultural labour force of developing countries.
Women bear also a very crucial role in nutrition. Researches have shown that well-being and health status
of pregnant and lactating women is crucial to determine child’s cognitive and physical development (Black,
Alderman, et al., 2013). In recent policy thinking, people talk about the so-called “window of opportunity”: a
period of 1000 days, between pregnancy and a child’s second birthday, during which good nutrition and
healthy growth have lasting benefit throughout lifetime. (Black, Victora, et al., 2013).
Provided women’s role in household livelihoods, researchers have investigated whether gender
empowerment programs, that increase women’s autonomy in decision making process, allow women to
reinforce their contribution for the household’s well-being, and a number of evidence provided support for
such an idea.
When, for instance, Case and Deaton (1998) in (Kurz & Johnson-welch, 2000) examines the relation between
income and access to food, they found that income controlled by women tends to be devoted to food
expenditure. Moreover, in a study about income and consumption in Ivory Coast, (Duflo and Udry (2004) in
(Sraboni et al., 2014) finds that increasing women’s share of cash income significantly increases the share of
households budget allocated to food.
With regards to women and agricultural production, (Ogato et al., 2009) argues that Ethiopian women
farmers who were given the same level of productive resources as male farmers, obtained 20% increased
yields than their counterparts. In addition, a study about Ghana(Doss, 2006) shows that women’s share of
assets, specifically, farmland, significantly increases budget share on food expenditure. Moreover, (Mason et
al., 2014) shows that when women own strategic resources such as livestock, food consumption reports
robust positive effect.
Additionally, an anthropological study from Uganda, found that women who engaged in non-agricultural
income-generating activities, such as local beer production, bored responsibility over this income and used
it to purchase nutrient-rich food for the household (e.g. beans, fish, nuts and seeds, tomatoes) (Dancause,
Akol, & Gray, 2010).
Empirical research has shown some evidence also as far as children nutrition is concerned. (Duflou 2003), for
example, shows that women tend to allocate more resources towards improving child nutrition than men
do. Qualitative analysis of studies from Latin America, showed how women openly declared to put their
children health at the top of their priorities. Handa (1996) in (Mason et al., 2014) reports that “a large portion
of women in Jamaica choose to maintain female-headed household based on the belief that their children’s
welfare would increase along with their personal consumption”.
1.2 Problem statement
Food and Nutrition Security can be assumed as dominated by two major determinants: assets and
preferences. As for female headed households, the effect of asset accumulation and preferences have two
opponent directions jointly in force in the system and whose resulted outcome determine the effect of
gender on food and nutrition security. Lack of asset has a negative sign while preferences for food has a
positive sign.
3
In this framework, I assume that gendered headship might either positively or negatively affect food and
nutrition security in female-headed households.
On the one hand, since female-headed household are often asset constraints, they are vulnerable and more
likely to be food and nutrition insecure than male-headed households. Therefore, the negative effect of lack
of assets would be greater than the positive effect of preference for food. Therefore, female headship would
result in a negative overall household food and nutrition security effect.
On the other hand, the effect of preferences for food is supposed to be positive. Women make choices more
closely linked to improve both the quantity and the quality of food consumption, and research have found
that such a higher preference for food is robust. Therefore, giving the chance for women to make choices on
assets, such as when they are head of a family, would lead to increase household food and nutrition security.
In such a perspective, women’s preference for food would compensate for assets limitation and result in a
overall positive effect of gendered headship on food and nutrition security.
Although many development programme and reduction poverty strategies focus on gender aspects for
improving household food and nutrition security in developing countries, the strength and direction of those
relationship are still limited understood. The relationship between gendered headship and food and nutrition
security remain unclear.
1.3 Research Questions
This work aims at estimating the impacts of gendered household headship on food and nutrition security. In
particular, I will investigate the relationship between gendered headship and food consumption as well as
nutritional outcome indicators in the framework of small-holders farmers in rural Tanzania. To achieve my
goal, I formulated the following general research question:

What is the impact of gendered headship and food and nutrition security in the breadbasket of
Tanzania?
And, I formulated the following sub-questions:




What are the current consumption pattern?
What are the current food consumption pattern?
How are the current consumption pattern linked with dietary diversity?
Do consumption and food consumption patterns as well as dietary diversity differs according to
gender of the household head?
This work proceeds as follows: Chapter 2 presents the theoretical framework. Chapter 3 provides an
introduction to the backgrounds and the study area. Chapter 4 contains methodology with models
definitions, data collection and materials, description and statistics of variables. Chapter 5 and 6 highlights
results and discussion while conclusions are given in Chapter 7.
4
Chapter 2
2 Theoretical Framework
The theoretical framework used for this thesis focused on the sustainable livelihood strategies and food and
nutrition related outcomes as well as on the standard theory of household decision.
2.1 Sustainable Livelihood Framework
The concept of livelihood refers to what people do for a living. Chambers and Conways introduced the
concept for the first time in 1991 defining livelihoods as “ [..] the capabilities, the assets (including both
material and social resources), and activities required for a means of living”. They also highlighted the
sustainability aspect of livelihoods. In their words “a livelihood is sustainable when it cope with and recover
from stresses and shocks, maintain or enhance assets, while not undermining the natural resource base”
(Chambers & Conway, 1992). This definition suggest that people engage in some livelihood strategies to
achieve preferred livelihood outcomes. The concept has been widely studied since the early ‘90 and it
became an approach with greater echoes in development studies (Scoones, 2009).
For a better understanding of such complex concept the Department for International Development (DFID),
developed a framework in 1991 (Department for International Development, 1999b). The sustainable
livelihoods framework emphasises that people’s life has to be conceptualized as a system (Scoones, 2009).
Such a system is composed of five components: the vulnerability context, the 5 assets pentagon,
transforming structures and processes, livelihood strategies and, finally, livelihood outcomes.
The core of the framework is the livelihoods pentagon, which defines five basic capitals or assets that people
rely on for achieving livelihood outcomes. Households’ portfolio of assets encompasses natural, physical,
financial, social and human capitals. Natural endowments, such as land and water, available in Nature are
examples of natural capital. Livelihoods gets increased by accumulation of this capital since the larger the
land size, the higher is the production and the higher the income. As a result, consumption is increased and
food security is assured.
Production inputs such as fertilizers as well as cattle are physical assets for a farming households that relay
on agriculture for survival. Those assets are used to increase income and improve living conditions, therefore,
a household that can use fertilizer on its land, or that can use cattle during production is assumed more likely
to have higher yield and income. Consumption as well as food security, therefore, better off.
Whether or not a household have money to spend and invest give an indication of the level of financial assets.
Example are consumer durables. Those can be used as proxy for financial assets with the assumption that
they can be exchange in the market. If this is the case, collecting financial capital allows households to
mobilise stock resources and transform them in flows to accumulate other assets. Thus, also financial capital
is assumed to have a positive effect on income, consumption and food security.
Household demographic characteristics, such as number of members or education level serve as an example
for human capital. Larger amounts of this capital ameliorates livelihoods. For instance, a household with
many members can imply more labour as production input. Moreover, in larger households non-productionrelated tasks such as water collection are assigned among a larger number of people. In this way, with respect
to smaller households, bigger households have ensured a greater number of functions essential for survival,
without paying the relative price. Therefore, I assume that human capital has a positive effect on income,
consumption and food security.
5
Finally, increased level of social capital help households to improve livelihoods. Social capital encompasses
from network relations to kinships. If households are better connected with more and more efficient
networks, they have access to more information or/and better resources. For instance, being part of powerful
farmer organizations, increases the probability of getting access to updated production systems or
management knowledge. Therefore, households linked to powerful groups are able to develop better
livelihoods. As a result, income, consumption and food security are assured. Finally, I assume that also social
capital bear a positive income effect in my systems.
Overall, the Sustainable Livelihood Framework suggests that the livelihood pentagon is crucial for survival. It
highlights that asset accumulation is important to develop good livelihoods strategies and to reach the
preferred outcomes. Since female-headed household are less likely to accumulate assets, it is often assumed
that they are worse off, when compared to men. Therefore, I assume that lack of assets bear a negative effect
on food and nutrition security.
2.1.1 Vulnerability context
It is important to take into account the external environment in which rural households live and operate,
indicated in the framework as “vulnerability context” (Department for International Development, 1999a) It
encompasses the economic, political and social trends in which households live in. It encompasses also
possible economic, social, political shocks. It is related to seasonal shift. Increased population density or
technological shifts are examples of trends in the vulnerability context. Humans and animals diseases as well
as war and political instability give an example of shocks. Whilst, example of seasonal shift can be price
fluctuations or lack of employment opportunities in certain period of the year. Rural households have limited
or no control over those factors, therefore, the vulnerability context has a great influence upon people’s life.
Trends, shocks and seasonal factors are important because they have a direct impact upon people’s assets.
Those can be both positive and negative. Shocks can destroy assets. For instance, floods can reduce yields,
while civil conflicts might lead to insecure roads making fields not accessible for farmers. Negative trends,
such as scarce availability of natural resources, or positive ones, such as technological innovation, can affect
the rate of economic returns.
The vulnerability context increases constraints faced by the poor. It makes poor household’s livelihood fragile
and this, in returns, makes people unable to cope with shocks and recover from stress. This also makes people
less able to influence the environment to reduce those stresses. Thus, poor households enter a vicious circus
where they become more and more vulnerable.
Vulnerability context shows that households can face a number of constraints upon which they have no
control. They have to cope with and recover from shocks. They have to adapt to seasonal shifts. Since it has
been highlighted that the vulnerability context is seen as negatively affecting the livelihood systems, I assume
that a negative effect bears also for female headed households.
2.1.2 Transforming structures and processes
Policies, law, institutions, the organization of the private sectors as well as societal norms are also
determinants of livelihood strategies. They are indicated as “transforming structures and processes” in the
framework (Department for International Development, 1999a). Structures are the organizations that set
and implement policies and legislations. They deliver services and enforce norms. They can be both private
and public. Structures operate at various levels, from individual to national. Governmental organizations,
private companies and churches are examples of structures.
6
Structures are important because they make processes functions. Legislations are defined by government
and enforced by courts. If legislation bodies and courts do not work properly, there are legislation gaps or
legislation is meaningless.
Culture and demographic characteristics are included in this area (Department for International
Development, 1999a). Historical and cultural backgrounds, kinships and power relationships are
encompassed in this part of the framework. Age, gender as well as caste systems are examples. These factors
give a particular status to people providing them with opportunities to engage in successful livelihoods
strategies or hindering such a process.
Structures and processes affect access to capitals (Department for International Development, 1999a). They
also define terms of exchange between different type of capitals. The different type of legal agreements are
examples. They influence the returns to the livelihoods strategies. Returns might be in terms of financial
capital. A concrete example of positive transforming structures and processes is a well- functioning markets,
which might lead to increased food and nutrition security. Returns might also be in terms of social or human
capital. Another example is the case of an established and widespread education system, which leads to
increased literacy rates or professional skills.
As it is indicated in the framework by a black arrow, structures and processes can influence (positively or
negatively) the vulnerability context since they affect trends and prevail shocks. For instance, when a wellfunctioning markets exist, the side effect of seasonality of production is reduced and shocks do not hit rural
farmers.
It is important to note that, likewise the vulnerability context, also transforming structures and processes are
often out of people’s control.
Structures and processes influence accumulation of assets as well as they affect the vulnerability context for
female-headed households. However, they can have either a positive or a negative effect on livelihood
outcomes. For instance, women might get lower wages then men because of custom norms that hinder them
from be employed in qualified jobs. However, women might also be better off than men when it comes to
self-employed job, such as small scale cookie trades or local brew production. As (Dancause et al., 2010) has
shown, for instance, women who produced local brew from sorghum, were responsible for the income
generated by this non-agricultural activity and decided to purchase nutrition food such as fish and beans.
Because brewing has traditionally been women’s work, the study showed that pastoralist women were able
to cope with shock due to shortage of cattle, taking advantage of the brewing costume to develop their
livelihood strategy to survival.
2.1.3 Livelihood strategies
Provided the vulnerability context, structures and processes, households define livelihood strategies
according to their needs and preferences. Livelihood strategies are the activities and choices people employ
in achieving livelihood outcomes (Department for International Development, 1999a). The framework
emphases that households engage in a variety of strategies. Therefore, assuming that a rural household is
engage in agricultural production do not exclude that the same household might have an off-farm business.
Another example is provided by remittances and migration. Farmers who earn their living from farming plots
in the rural areas, might also receive remittances from relatives working in the city.
A strategy is formed by different activities and those ones are defined based on the assets available.
(Department for International Development, 1999a). Therefore, households that are equipped with more
assets are able to choose from a larger range of options in order to set their strategies. As a result, those
ones are more likely to maximise livelihood outcomes.
7
2.1.4 Livelihood outcomes
Households use livelihood strategies to achieve livelihood outcomes. Outcomes encompasses a very
diversified type of goals, from improving income level to achieve well-being or reducing vulnerability. As
mention before, food and nutrition security is one of the outcomes that households might want to achieve.
Malnutrition is therefore, seen as a failure in the achievement of the desired outcome.
The most important contribution of the livelihood approach and of the sustainable livelihoods framework is
that livelihood outcomes can vary among different individual and social groups. Outcomes can even conflict
one with another (Department for International Development, 1999a). Therefore, households who engage
in a certain strategy in order to achieve a certain outcome, might hinder, at the same time, the realization of
another outcome. A typical example is the case of a livelihood strategy that aims at increase income might
be detrimental for natural environment. Another example is linked to intra-household relations, where the
livelihood strategy of a member who seeks to decrease household vulnerability conflicts with the strategy of
another member who aims at maximise income.
Since assets are important to achieve a certain outcome, people seek to engage in sustainable strategies,
which assure accumulation of assets over time. They try to trigger a virtuous circle: the more assets they
have, the more likely they are to cope with stress and recover from shock. This allow people to differentiate
their strategies in order to achieve multiple outcomes. Those outcomes might not be al maximized but they
might be less than optimal in order to be sustainable over time.
Sustainability is not to be intended only in terms of environment, but it has to be linked to power relations.
For instance, an individual within a household might be involved in strategies that are not optimal in terms
of income level, in order to maintain a powerful position.
In this research, I will investigate what determines food insecurity in rural households in rural Tanzania.
FIGURE 1 SUSTAINABLE LIVELIHOOD FRAMEWORK
SOURCE: 1 (Department for International Development, 1999a)
8
Note: the arrows indicate a relationship between components. They do not imply causality but serve to
highlight a certain level of influence.
2.2 Economic theory of household decision
The standard economic theory of household decisions states that household use resources (capitals) to
achieve the highest level of utility possible (Varian & Repcheck, 2010). In this framework, household
consumption decisions are determined by household income level, preferences and market prices. The utility
maximization problem can also be seen as an expenditure minimization problem (Peerlings, 2014). Due to
the budget constraints, consumers choose the combination of goods that maximise utility while minimize
expenditure. Therefore, demand of goods varies according to preferences and budget.
Following the standard economic theory of household decisions, the following assumptions are part of this
study:
-
-
Each household has only one utility function. Resources are pooled according to some kind of sharing
rule assuring that no one can be better off without making someone else worse off. Households
achieve a Pareto efficiency thanks to this collaborative model in resource allocation. This means that
all members are assumed to jointly maximize the same household level of welfare and the same
utility function (Doss, 2013).
Household definition: when food is prepared and shared daily among a group of people, those ones
are members of a household. External members are those who do not share daily meal with other
members.
The following chapters explore the relation between gendered headship, food consumption and nutritional
outcomes in rural Tanzania.
9
Chapter 3
3 Background
This study is part of an impact evaluation research financed by the AGRA consortium, with the support of the
consultancy company 3EI and the Development Economics chair group of Wageningen University and
Research (WUR).
The impact evaluation research, titled “Increasing Agricultural Productivity in the Breadbasket Area of
Southern Tanzania”, is implemented by SVN Netherland Development Cooperation and aims at the following
objectives:




To strengthen the capacity and the efficiency of farmer organization in the target district;
To increase smallholder led agricultural production;
To enhance smallholder farmer access to structure produce market
To improve access to extension and advisory service among smallholder farmers and the private
sector;
3.1 Study area
Tanzania geography and major historical facts
Tanzania is located in East Africa, neighbouring Kenya, Uganda at North, Rwanda, Burundi, Democratic
Republic of the Congo and Zambia at West, Malawi and Mozambique at South and the Indian Ocean at East.
It covers an area of 947,300 square km and has 51.82 million of inhabitants (World Bank, 2015). Climate in
Tanzania is substantially warm. Two different rainfall patterns distinguish Norther and Southern regions. The
northern and northern coastal areas have two rainy seasons with soft rainfalls between October and
December. More intense rainfalls are between March and May. The southern, central and western parts of
the country exhibit one rainy season from November to April (Mbululo & Nyihirani, 2012).
Politically Tanzania is one of the most stable country in Africa. It became independent in 1961 when the
leader of the revolution, Julius Nyerere, led the country into a socialist system with one party state model.
The government guided by Nyerere initiated a reform era under the umbrella of the “Ujaama Socialism”
programme (“family” in Swahili). This model focused on economic reforms and it meant primarily to align
Tanzanian agricultural sector with the socialist agricultural model (F. Ellis & Mdoe, 2003). A “villagization”
programme was introduced to gather farmers, previously scattered all over the vast territory, into new
administrative and production units. The state took the comprehensive control of agricultural prices and
markets (F. Ellis & Mdoe, 2003). Extended nationalization reforms were implemented in agriculture,
industrial and services sectors. Along with the establishment of Swahili as national language, Nyerere
reduced the disconnection of the more than 120 tribes and ethnic groups and unified the population under
one only flag and government. From its independence, Tanzania have known political stability (F. Ellis &
Mdoe, 2003), which did not coincide with economic prosperity. Alike many other African countries, in the
‘80s, Tanzania knew a period of devastating crisis that forces the government to adopt a severe structural
adjustment programme based on massive economic liberalizations starting from 1986.
Since 2000, the economy have exhibited a robust growth rate between 5% and 7% per years. Although the
high rate of economic growth in recent years, yet poverty remains high and widespread (The World Bank,
10
2011). About 28,2% of population lived below the poverty line in 2011, and per capita gross domestic product
(GDP) in purchasing power parity (PPP) was $1,700 in 2012 (Cochrane & Souza, 2015).
Agriculture and food consumption
Agriculture is the backbone of the country’s economy. It contributes for 27% of the GDP of Tanzania and the
relatively mild weather condition allow Tanzania to grow a great variety of crops. The majority of Tanzanian
population lives in rural areas and 80% of the labour force is employed in agriculture (Cochrane & Souza,
2015). From this sector, 80% of the poor in Tanzania derive their livelihoods (A. Ellis et al., 2007).
The majority of the agricultural products are consumed domestically. Rural small-holder produce mainly for
their own consumption.
In order to illustrate food consumption pattern of a country as a whole, it is generally agreed that food
balance sheets are the most appropriate source of information ((FAO) Food and Agriculture Organization of
the United Nations, 2002).
Data showed in fig. 3 provide estimates of quantities of food available for human consumption in Tanzania
in 2013.
FIGURE 2 FOOD BALANCE SHEETS IN TANZANIA, 2013
Food balance sheets in 2013
consumption in kg/capita/yr
160
140
120
100
80
60
40
20
0
SOURCE: 2 (FAOSTAT, 2016)
At aggregate level, the top three food commodities consumed by Tanzanian people are starchy roots, cereals
and fruits. Per capita consumption of starchy roots is 140 kg per year, while cereals, excluding those involved
in beer production, count for around 100 kg/capita/yr. Among the least available food items there are pulses,
meat and fish. Per capita consumption of those food groups are less than 20 kg/capita/yr.
As the table shows, Tanzanian diet rely primarily on starchy roots and cereals, which are caloric intense, but
poor in nutrients. Among tubers, cassava and sweet potatoes are greatly available. Important nutrients such
as protein and vitamins are contained in meat, fish and milk, whose availability is scares. Meat, for instance,
covers only 2% of the food supply in Tanzania (FAOSTAT, 2014). Low availability is likely to make consumption
also low, casting a problem for food and nutrition security in the country.
Nutrition Security in Tanzania is very critical. FAO (FAOSTAT, 2014) indicates that in 2010-2012 the
prevalence of undernourished people in Tanzania reached 39%, hitting mainly rural population. Provided that
rural households represents 72% of the population in 2014, the risk of malnourishment is particularly severe.
11
Also under the point of view of dietary diversity, Tanzania has to face major problems, especially for rural
women. As the study of (Keding, Msuya, Maass, & Krawinkel, 2012) suggested, women in Tanzania have a
median of 6 food groups, with one third of the study participants having a dietary diversity of two to four
food groups per day. Moreover, the study suggested that rural women have basic diet consisting of cereals
and vegetables, which are poor in important macronutrients such as proteins and fats. This indicates that
overall Tanzanian households have a poor dietary quality.
Women and employment
In Tanzania, the percentage of women employed in agricultural production is pretty high, reaching about
54% of the agricultural labour force in 2010 (FAOSTAT, 2014).
Out to the 16.9 million people actively employed in 2006, approximately 50% are women (A. Ellis et al., 2007).
Across the sectors, the participation rate of women to the labour force is slightly higher in agriculture and
trade, where are employed 52% and 55% of women. Men appear to have higher participation rate in
manufactories, constructions, transportation and finance. (A. Ellis et al., 2007).
Although their participation in the labour market as employee reaches 50% of women, they are less likely to
have a paid job, either in formal or informal sector, when compared to man (A. Ellis et al., 2007).
Provided that the majority of women are engaged in agricultural production for survival, it is interesting to
look at the relation between female farmers and agricultural assets.
Women and Assets
Women in Tanzania have very limited access to important assets, especially production assets and inputs.
Land is the most important among assets since it is not only used as production input but it also bears other
economic and social functions. Land in Tanzania is vital because of the pivotal role of agriculture within the
economy, the importance of increased agricultural production as a poverty reduction strategy and its
function as collaterals. Land is also seen as a symbol of prestige and power (A. Ellis et al., 2007).
Although these crucial roles, women are estimated to own only one-fifth, or about 19 percent of titled land
in Tanzania, with an average land holding size of 0.30 hectares, compared to 0.70 hectares for men (Bureau
of Statistics 1994 in (A. Ellis et al., 2007)). Provided that women contribute for slightly more than half of the
agricultural production at the national level, this data suggest that women often work on lands upon which
they hold no right legally.
Uncertain land rights reduce the likelihood of agricultural investments (A. Ellis et al., 2007). This holds
especially for poor farmers such as women. For instance, investing in soil quality, in irrigation systems or in
crops of a higher value has economic rationale only if the property of land remain in the availability of the
farmer. In this way, he or she can enjoy the return of the investment. If, on the contrary, land rights are
uncertain, then farmers might need to leave their land and the improvements made might be gone with it.
Therefore, they might not see the value of making such investments in the first place. Additionally, not only
uncertain land rights cast a problem for returns of investments, but also, farmers who cannot claim the
property of the land that assures their survival, are more vulnerable to shock and negative trends, than those
whose land is secure (A. Ellis et al., 2007).
Moreover, although several laws formally protect women’s claim on land, customary norms in Tanzania tend
to protect clan land from being inherited by other clans and kinships. Moreover, customary norms tend to
assure control of properties on men. For example, if the husband dies, his relatives often take away from the
widow and children, if any, valuable agricultural inputs such as livestock as well as basic property such as
furniture and other household items. Such circumstances keep influencing the decision-making process of
land ownership and control, in particular in the rural areas, in the recent years (A. Ellis et al., 2007).
12
Women have also very limited access to other production resources such as fertilizers, credit and agricultural
trainings. Ninety-one percent of women farmers in Zanzibar North, for example, do not use agricultural
inputs at all, 86 % do not have access to formal means of credit, and 80 % have no access to extension
services. Those numbers suggest that women in rural areas encounter high constraints in increasing
productivity and reaching high yields that would assure survival to their self and their households (A. Ellis et
al., 2007).
Not only women are “poor” in assets, but also in “time”, compared to men. A study about banana and coffee
farmers in the region of Kageram, in the north-west of Tanzania, showed that women provided 52 percent
of the labour for economic activity, compared with 42 percent for men and that men were estimated to have
4.5 hours of leisure time per day, compared with 2 hours per day for women. (A. Ellis et al., 2007). Time
constraints is a severe problem for Tanzanian women not only because this lower their productivity and
yields, but also it reduces the ability of women to engage in market work, while their labour effort is not fully
captured in national accounts. As a study (Seen 2006) found, if Tanzanian women reduce time spent for chore
by 10% (1 hr of our 10), their probability to engage in an off-farm business would increase by 7% (A. Ellis et
al., 2007). If women were able to engage in off-farm activities, their income would increase with a
considerable advantage in terms of their food and nutrition security.
FIGURE 3 - MAP OF UNITED REPUBLIC OF TANZANIA AND DISTRICTS
13
Chapter 4
4 Methodology
4.1 Models
In this chapter, my models are explained in details. Particularly, I will answer the following sub-questions:



What is the effect of gendered headship on household consumption?
What is the effect of gendered headship on household food consumption pattern?
What is the effect of gendered headship on household diet adequacy?
4.1.1 Model 1: Gender and Households Consumption
“What is the effect of gendered headship on household consumption?”
To begin with, I explored the determinants of household income. Income is a tricky indicator. Gathering
robust information about real income is very difficult because households have multiple sources to make
their leavings. For these reasons, estimating income through a questionnaire is a complex operation that
costs money and time. Income, as a measure of household wealth, is quite stable over time and does not
allow capturing small changes in the short run. Therefore, household consumption is considered a more
reliable indicator of household wealth (Quisumbing et al., 2001).
Following the livelihood framework, I assumed that the level of income depends on the amount and type of
assets owned by the household. Out of the five assets indicated by the framework, I used four, namely:
human (H), physical (P), financial (F), and natural (N) capitals. Social capital is missing since the data available
did not cover this information. Following (Apple 2015), I assumed that social capital is positively related to
natural and physical capital. Therefore, estimating variables related to those two capitals, I was able to
capture the influence of social capital.
In order to explore what are the determinants of income, I used Ordinary Least Square (OLS) regression with
robust standard errors. I formulated the following relation:
𝑃𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 𝑓(𝐻, 𝑃, 𝐹, 𝑁)
(1)
My model can be written as:
ln 𝑌 = 𝛼0 + 𝛼1 𝐺 + 𝛼2 𝑆𝑖𝑧𝑒 + 𝛼3 𝐷𝑒𝑝 + 𝛼4 𝐴𝑔𝑒 + 𝛼5 𝐴𝑔𝑒𝑆𝑞 + 𝛼6 𝐸𝑑𝑢1 + 𝛼7 𝐸𝑑𝑢2 + 𝛼8 log 𝐿𝑎𝑛𝑑 +
𝛼9 𝐷𝑢𝑎𝑏 + 𝛼10 𝐶𝑎𝑡𝑡𝑙𝑒 + 𝛼11 𝐸𝑞𝑢𝑖 + 𝜀
(2)
Where LnY is logarithm of per capital total consumption expenditure used as proxy for “permanent income”;
G is gender of the household head while Size is household size and Dep is Dependency ratio. Age and Agesq
indicates the age of the household head (linear and non-linear relation), Edu1 and Edu2, are two dummy
variables for education level of household head and Land is land size. Duab is durables owed by the household
while Cattle is livestock size and Equi is a vector of production equipment. Definitions and in-depth
description of variables are given in the next section (4.3). Finally, 𝜀 is the error term; and , 𝛼0−11 , are
parameters to be estimated.
14
4.1.2 Model 2: Gender and Food Consumption Pattern
“What is the effect of gendered headship on household food consumption pattern?”
In my second step, I investigated the relationship between food consumption pattern and gendered
headship. I looked at how households make use of their economic resources to get access to food, analysing
their food expenditure pattern. In order to get an idea about the relative importance of food in the overall
consumption, I analysed the overall food consumption and I compared it with overall consumption. Then, in
order to explore food consumption patterns, I studied food consumption shares dedicated to specific food
groups.
I developed my models as follows:
𝑃𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 𝐹𝑜𝑜𝑑 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 𝑓(𝑇𝑜𝑡𝐶𝑜𝑛, 𝐺, 𝐻)
(3)
𝐹𝑜𝑜𝑑 𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑆ℎ𝑎𝑟𝑒𝑠 = 𝑓(𝑇𝑜𝑡𝐹𝑜𝑜𝑑𝐶𝑜𝑛, 𝐺, 𝐻)
(4)
Particularly, I can re-write my model as follows:
ln 𝑉 = 𝛽0 + 𝛽1 ln 𝑌 + 𝛽2 𝐺 + 𝛽3 𝑆𝑖𝑧𝑒 + 𝛽4 𝐷𝑒𝑝 + 𝛽5 𝐴𝑔𝑒 + 𝛽6 𝐴𝑔𝑒𝑆𝑞 + 𝛽7 𝐸𝑑𝑢1 + 𝛽8 𝐸𝑑𝑢2 + 𝜃
(5)
Where ln 𝑉 is the logarithm of per capita Food Consumption expenditures; ln 𝑌 is logarithm of per capita
Total Consumption and the other variables are covariates defined as in model 1 (previous paragraph); 𝜃 is
the error term; and 𝛽0−8 are parameters to be estimated.
Moreover, also Food Consumption Shares can re-write as follows:
ln 𝑇𝑖 = 𝛾𝑖0 + 𝛾𝑖1 ln 𝑉 + 𝛾𝑖2 𝐺 + 𝛾𝑖3 𝑆𝑖𝑧𝑒 + 𝛾𝑖4 𝐷𝑒𝑝 + 𝛾𝑖5 𝐴𝑔𝑒 + 𝛾𝑖6 𝐴𝑔𝑒𝑆𝑞 + 𝛾𝑖7 𝐸𝑑𝑢1 + 𝛾𝑖8 𝐸𝑑𝑢2 + 𝜗 (6)
Where ln 𝑇𝑖 is one of the Food Consumption Shares of specific food groups (as indicate in Tab. 1, below), ln 𝑉
is logarithm of per capita total Food Consumption expenditures used as proxy for “permanent income”
(Quisumbing et al., 2001); The rest of the variables are defined as in model 1. 𝜗 is the error term; and 𝛾0−8 ,
are parameters to be estimated.
My models are estimated with two approaches. I used first OLS regression and, afterwards, I tried an
Instrumental Variable (IV) approach in order to control for endogeneity of total consumption expenditure
variables.
4.1.3 Model 3: Gender and Dietary Diversity
“What is the effect of gendered headship on household diet adequacy?”
I used an ordered probit regression in order to estimate the probability of women reporting a low, medium
or high variety in diet.
15
The probit (probability) model is used when researchers want to investigate the probability of events
occurring. The ordered probit model assumes that it can be said something about the order of such events.
For instance, ordered probit models are used when the dependent variable is part of a rating systems such
as (poor, fair excellent). It is used to analyse opinion surveys whose outcome is, for instance, agree, neutral,
disagree. It implies that the events can be categorized and ordered according to specific principles. It
estimates the values of the probability that the dependent variable falls into one of the categories rather
than another. It predicts such a probability value by modelling the relationship between the ordered
categories of a dependent variable and a set of explanatory variables (or predictors) (Lokosang, Ramroop, &
Hendriks, 2011).
One of the aim of my research is to investigate the likelihood of women experiencing low, medium or high
dietary adequacy. Moreover, my goal is to investigate how such a likelihood value relates to income, gender
of the household head and households characteristics. Therefore, the ordered probit procedure is the proper
estimation technique to use.
In this type of model, the predicted probability of the event Y, given the X, is estimated as follows:
𝑝𝑟[ 𝑌𝑖 = 1|𝑥] = 𝛷(𝑓(𝑥))
(7)
Where, 𝑌𝑖 is the probability of the dependent variable; 𝑓(𝑥) is the function of the vector X of explanatory
variables and Φ (. ) is the standard normal cumulative distribution function, defined as follows:
𝑥
Φ (x) = ∫−∞ 𝜑(𝑧)𝑑𝑧
with
𝜑(𝑧)=
2
1
𝑒 −𝑧 ⁄2
2𝜋
√
(8)
As for my specific research question, 𝑌𝑖 is the predicted probability that women’s dietary diversity score falls
into either the low, or medium or high category. 𝑓(𝑥) is function n.2.
In practice, I used OPROBIT module in STATA 13 (Cameron Colin & Pravin Trivedi, 2009).
4.2 Data Collection and Materials
This study collected data in the region of Mbeya (Fig. 3), Tanzania, over four districts, namely, Mbozi, Momba,
Mbarali and Mbeya Rural between December 2014 and February 2015.
A PhD student from Wageningen University coordinated the survey supported by 6 supervisors and 30
enumerators. Enumerators were all Tanzanian and they could speak Kiswahili. All respondents could
sufficiently speak Kiswahili too. Therefore, there was no need for interpreters.
Cross-sectional data were collected over 1648 randomly selected households using a structured survey
questionnaire. However, cases of missing entries were removed and the actual working sample size was 1627
households. The survey was split into two parts. One addressed issues of agricultural production, adoption
of agricultural innovation and labour and it was assigned to the household member primarily responsible for
making decision about crops. In most of the cases, it was a male. The other part of the survey contained
questions about household’s demographic characteristics, assets, income and expenditure level, food
insecurity, nutrition insecurity, coping strategies and women empowerment. The household member
responsible for chores answered questions from these modules. In most cases, it was a female member,
primarily the wife. The two surveys were conducted separately but simultaneously.
When the head of the household was not available for the interview, the survey was completed by the eldest
member of the household.
16
The randomly selected households were identified based on the criterion that they were part of at least one
farmer organization in the four districts of interest.
Hard copy questionnaires and tablets ASUS were used to administer the survey. Open Data Kit (ODK) software
was used for collecting the data while the analysis was conducted using STATA 13 as statistical software.
Four districts were selected for this research, namely Mbeya, Mbosi, Momba, Mbarali. In each district, overall
51 eligible wards were identified by the Impact Evaluation research Team. In each wards, a list of Farmer
Organizations was used to reach households. In principle, all the existing FOs of which the team was aware
of, were included in the study. Finally, households were randomly selected from the FOs’ list.
Some limitations in the sampling and data collection occurred during the field work. Some FOs had just been
formed and they were not well-organized yet. Therefore, they did not know very well each others and they
could not be of any help for the enumerators to identify FOs missing members. Moreover, some members
were not reached at all since they dropped their membership, died or moved out to another district. In order
to compensate, the Evaluation Team replaced them by randomly selected other members from FO list. Due
to the weather conditions and lack of efficient infrastructures hinder the baseline research team from the
selected households, therefore efficiency is not optimal.
4.3 Variables description
4.3.1 Measuring Food and Nutrition Security
In this work, food and nutrition security is analysed using food consumption behaviour and nutritional
outcomes. In particular, two set of measures are used: 1) per capita total consumption, 2) per capita food
consumption 3) Women Dietary Diversity Score.
Per capita total consumption was calculated computing amounts of food and non-food consumption divided
it by household size. As for non-food consumption I aggregated the consumption expenditure of 11 groups
of expenditures, namely: i) school fees, ii) other education expenditures, iii) housing (construction repairs),
iv) furniture and appliances, v) insurance, vi) wedding, vii) clothes/shoes, viii) funerals, ix) festivals, x) church
donations/charity, xi) loan. Expenditure were reported in different time units. Some of them were reported
in year, such as school fee, some in months, such as loan or insurance. At the stage of analysis, I computed
the year amounts.
Per capita total food consumption was calculated by summing consumption expenditures of 49 food items.
The targeted respondent was the household members normally responsible for food preparation. The
reference time was the day before the interview. For male-headed household, the targeted respondent was
the wife of the household head. I case of female-headed household, it was the household head her-self. Data
were collected using a seven-day recall battery of questions about consumption quantities and prices. To
come up with the total food consumption for 1 year, I multiplied the collected consumption quantities times
32 weeks. I included also the values of home-produced food and food donations. To compute the value of
such foods, I took the median prices reported by respondents per food item and per unit of measurement
and multiplied them by reported consumption quantities. Then I calculated the values per year.
17
I aggregated the 49 food items into eight main food groups (tab.) as follows: i) cereal and other starched, ii)
meat, fish, milk and other protein-rich food, iii) vegetables and fruits, iv) nuts, fats, oils and condiments, (v)
sugar and non-alcoholic beverages, vi) alcoholic beverages. Note that even though alcoholic beverages are
often excluded by measurements related to diet, data about alcohol are comprised in analysis on
expenditures. All monetary variables are expressed in Tanzanian Shillings where the reference year is 2013.
Household food consumption expenditure of individual food groups are estimated together with total
consumption expenditures since it can be useful to address substitution effects (Bertelli & Macours, 2014)
TABLE 1 - FOOD CONSUMPTION SHARE - AGGREGATION INTO FOOD GROUPS
Group food n.
1
2
3
4
5
6
Food Expenditure Share - aggregation on food groups
Description
Food group
rice, maize (flour, cob and grain),
Cereals and other starches
wheat, sorghum and millet (flour and
beef, chicken, pork, goat, wild birds,
Protein rich food
fish, milk- milk, butter and eggs
onion,tomatoes carrot, green pepper,
spinach,cabbage,wild vegetables, citrus
Vegetables and Fruits
fruits (orangesm lemon ect) mangoes,
avocado, ripe bananas
groundnuts in sell, coconuts,
cashewnuts, other nuts, cacooking oil, Fats and olis, other condiments and Nuts
salt
cakes and biscuits, sweets,sugar, tea,
Sugar and non- alcoholic beverages
coffee, sodas, sugarcane
beer, local brews and other alcoholic
beverages
Alcoholic beverages
As for food consumption shares, I computed the consumption expenditure share of individual food groups
as the percentage of each food item expenditure over total food expenditure.
Rural women are often one of the most vulnerable population in developing countries (Idalinya, Bauer;, &
Siddig;, 1987). As such, their nutritional status is taken as a reference in order to estimate the nutritional
status of the whole household, under the assumption that households where women show good nutritional
status are likely to show good nutritional figures too. Moreover, investigating the diet is the simplest way to
investigate nutritional status (Bhutta et al., 2013).
Women Dietary Diversity Score (WDDS) is an indicator of household dietary adequacy. Specifically, the
greater the variety of women’s diet, the larger the probabilities that household consume nutrients essential
for an active and healthy life. Thus, it gives an estimation of the household health status (FAO Nutrition and
Consumer Protection Division, 2008).
The Women Dietary Diversity Score is estimated based on (FAO Nutrition and Consumer Protection Division,
2008). Data were collected with a semi-quantitative 24-hours recall procedure of food consumption, since
studies have shown that the quantity of food consumed are recalled more precisely than a food frequency
questionnaire and this method had already been successfully applied to dietary diversity scores calculation
in African context. (Savy, Martin-Prével, Traissac, & Delpeuch, 2007). Women in reproductive age were asked
to freely recall what was eaten the day and night of the day previous of the data collection, starting with the
first food/beverage at the morning till the last item in the evening. Enumerators have been instructed in
probe for snacks or other food eaten away from home at the end of the free recall. Mentioned food were
grouped into 17 food categories. At the analysis level, those food groups have been aggregated into 11 food
groups of which 9 have been selected to construct WDDS based on a nutritional criteria. Namely: i) Starchy
staples, ii)Dark green leafy vegetables, iii) Other vitamin A rich fruits and vegetables, iv) other fruits and
vegetables, v) organ meat, vi) meat and fish, vii) eggs, viii) legumes nuts and seeds, ix) milk and milk products.
18
Each of the food groups have a weight of 1. Neither the frequency of consumption nor the amount of food
consumed was taken into consideration.
TABLE 2 - WDDS - AGGREGATION OF FOOD CATEGORIES
Food categories
Cereals
Bread
WDDS - Aggregation of food categories
Description
Rice, maize, sorghum, millet, wheat
Bread
White roots and tubers
Potatoes, Cassava, Yam and cooked bananas other roots/tubers
Dark green leafy
vegetables
Dark green leafy vegetables, including wild forms + locally available vitamin A
rich leaves such as Spinach
Vitamin A rich
vegetables and tubers
Pumpkin, Carrot, Squash, or Sweet Potato that are orange inside + other locally
available vitamin A rich vegetables
Vitamin A rich fruits
Fresh and dried. Ripe Mango, Ripe papaya, Bananas and 100% fruit juice from
these + other locally available Vitamin A rich fruits
Others vegetables
Other vegetables (e.g. Tomato, Onion, Eggplant, Green beans, Cabbage)
Other fruits
Organ meat
Flesh meats
Fish and seafood
Eggs
Legumes, nuts, seeds
Milk and milk products
Other fruits, including wild fruits and 100% fruit juice made from these
Liver, Kidney, heart or other organ meats or blood-based foods
Goat, Beef, Lamb, Chicken, Duck
Fresh or dried fish
Eggs from Chicken, Duck, or any other bird
Beans, lentils, peas, nuts (peanuts)
Milk, Cheese, Yogurt or other milk product
Oil and fats
butter, vegetable oil added to food or used for cooking
Sweets
Spices, condiments,
beverages
Sugar, Honey, Sweetened soda or sweetened juice drinks, Sugary foods such as
Chocolates, Candies, Biscuits and Cakes
Spices (Black Pepper, Salt), Condiments (SKetchup), Coffee, Tea, Alcoholic
BEVERAGE
Food gruops
Starchy staples
Dark green leafy
vegetables
Other vitamins A rich fruits
and vegetables
Other Fruits and
vegetables
Organ meat
Meat and fish
Eggs
Legumes, nuts seeds
Milk and milk products
Collected but not used for
WDDS
Collected but not used for
WDDS
The WDDS scores from 0 to 9 and it is reflects the probability of micronutrients adequacy in the diet (FAO
Nutrition and Consumer Protection Division, 2008). The score was grouped into tertiles to distinguish
between diets of low, medium and high diversity. There is no agreement in the scientific community about
how many food groups should be part of each tertiles. It is generally based on the final purpose of score
constructing (Keding et al., 2012). For my case study, I took as reference the study made by (Savy et al., 2007)
on Dietary Diversity Scores of women in childbearing age in Burkina Faso, calculated over 1-day and 3-days
recall period with 9 food groups. I constructed the WDDS in tertiles and defined as follows:
-
households whose elder woman consumes between 1 and 2 food groups a day were classified as
having a diet low in micronutrients;
households whose elder women consumes 3 groups in the previous 24 hours, were said to have a
diet with medium amount of micronutrients
finally, households whose woman consumption is between 4 and 9 food groups, were classifies as
having high diet rich in micronutrients.
It should be noted that, studies have shown that fats and oils do not bring about micronutrients in the diet,
therefore this food group is not part of the WDDS (FAO Nutrition and Consumer Protection Division, 2008).
The distribution of WDDS (Fig. 6) shows that 45% of women reported consumption of 3 food groups, while
about one third of them have eaten between 1 and 2 food groups (29%) and between 4 and 7 food groups
19
(26%). This makes the WDDS likewise normally distributed, which helps the efficiency of the analysis.
Moreover, although during the methodology definition phase, the WDDS grouping criteria was based on 9
food groups, at the data analysis phase, I found that some food groups, such as organ meat, showed zero
observations, since no women have reported consumption of that particular food. Therefore, none of the
respondents had consumed all food groups, and the WDDS, in reality, ranges between 1 and 7.
Distribution of Dietary Diversity Score
800
Frequencies and cut-offs for tertiles
Medium (45%)
High (26%)
0
Frequency
200
400
600
Low (29%)
0
1
2
3
4
WDDS
5
6
7
FIGURE 4 - DISTRIBUTION OF DIETARY DIVERSITY SCORE
4.3.2 Households demographic characteristics
In my research, a group of people form a household when there are blood bounds between them or they
share food on a regular basis. Therefore, a friend that consume meals with the household members is
considered as part of the family. At the same time, children and adolescences who were sent to school, are
included in the analysis.
The vector of the demographic characteristics includes household size, household head age (linear and nonlinear relationship), household head education level and dependency ratio.
The head of the household was identified as the person who was indicated as the household head in the
Farmer Organization list. I assumed that the household head is the person responsible for the most of the
household’s decision, concerning both production and consumption.
Data regarding the household head education status were collected with a category variable indicating
eleven levels of education from “none” till “higher education and training”.
20
This variable have been split in two distinct dummy variables. One indicating whether the head of the
household have completed at least primary school and another indicating whether he or she has completed
at least secondary school. As for the first dummy, the variable has been assigned one if at least primary school
is completed, zero if household head reports lower level of education. As far as the second dummy, the value
is equal to one if the level of education is at least secondary, while zero if it is lower.
Dependency ratio is defined as the proportion of the non-working age individuals (age between 0-14 and 60or higher) over the working age population (15-59 years) in percentage.
The rationale behind those variables is based on the assumption that they are likely to affect food choices
and therefore food and nutrition security outcomes. For example, adults usually consume more food than
children do, so I controlled for the household composition characteristics by the household size and
dependency ratio variables.
Definition of Asset Owned and Agricultural
equipment variables
Asset Owned
Phone
Radio
Tv
Generator
Solar panel
Bike
Morobike
Car
Improved stove
Panga knife
Hoe
Agricultural equipment
Warehouse
Irrigation system
Ox
Plough
Planter
Ridger
Harvester
Power tiller
Grain mill
Packaging
Hand equipment
4.3.3 Other households’ characteristics
Assets owned. This variable indicates the total median
monetary value of assets owned by the household. For each
asset, I computed the total monetary value as the monetary
value of a single asset times the number of asset owned.
Then, I identified the median of the total monetary value.
Agricultural equipment. The equipment used to compute
this variable are agricultural production factors owed by the
households. Assets and agricultural equipment included in
the variables definition are described in table 7. Before
starting the analysis, data cleaning was performed. Data
cleanings was done to reduce the effects of outliers and
improve variance’s estimation. Moreover, the sample has
been clustered and weighted in order to reduce the
influence of geographic-related differences among
household.
4.4 Descriptive statistics
In table 3, descriptive statistics of my sample are provided. In the sample, the majority of the households are
male-headed. Families where the head of the household is a man are 1400, while 227 are the family with a
woman as head. Households in the sample spend, on average, about 3 million Tanzanian Shillings year round
for general expenditure such as schooling, charity, furniture and others. They spend slightly less than 2 million
on food. In other words, a typical household spend about two thirds of its budget on food, indicating that
food is a necessity. Independent sample t-test of means differences reveals that, on average, male-headed
households show significantly higher overall total expenditure and overall total food expenditures. They own
more livestock and they have larger land size than compared to female-headed households. Moreover, maleheaded households have significantly larger household size.
As it showed by the table 3, the independent sample T-test of dependency ratio, the age of the household
head (linear and squared) are not significantly different from zero between female and male-headed
households. This means that there is no difference in means between those two subgroups. Thus,
dependency ratio and household head age are, on average, similar between male and female headed
households.
21
22
TABLE 3 - DESCRIPTIVE STATISTICS OF VARIABLE
Male
Variable
Total food
consumption
Total
consumption
Description
Annual total food
consumption
expenditure in
Tanzanian Shillings.
Year of reference
2013
Annual total
consumption
expenditure in
Tanzanian Shillings.
Year of reference
2013
Land size
obs.
Mean (SD)
Obs
1,993,320
continuous
1400
(1,429,020)
1400
227
227
1400
total number of
plots in acres
continuous
logarithm of
land size
1399
2.889
1.097
226
7.619
1396
1.567
225
continuous
Dependecy ratio
continuous
1400
5.610
224
227
Age of the
household head
- squared
in years
1400
1400
49.08
Whether the
1 = primary or
Household head household head higher
primary school completed at least
level
primary level of
0 = lower
education
Whether the
1 = secondary or
Household head
household head higher
secondary or
completed at least
higher school
secondary level of
level
0 = lower
education
frequencies %
227
6.950
Obs. (%)
frequencies %
227
57.3660***
35.24
11.00
5.73
227
89.00
Chi-squared values
64.76
14.64
1400
-0.9284
(0.870)
85.36
1400
-0.7845
(12.100)
(0.930)
Obs. (%)
1.2688
227
6.890
10.8066***
(27.365)
(13.031)
continuous
7.3425***
42.822
48.35
continuous
3.990
227
(21.154)
in years
1.097
(2.090)
45.235
Age of the
household head
2.7500***
(0.893)
(2.163)
1399
5.260
(11.640)
(0.854)
Total number of
Household size people living in the
household
3.5420***
(5.371)
(13.670)
continuous
2.6771 ***
(0.870)
(6.342)
1397
2.6937***
227
2.498
continuous
2.5078***
(3,880,602)
(0.900)
Total number of
cattle owned by the
household
(1,497,526)
2,851,099
(9,926,284)
range 1 - 9
Indipendent Sample ttest: (Ho: mean(Male)mean(Female)
1,726,294
3,847,124
continuous
Test Statistics
Mean (SD)
3.058
Women Dietary
Diversity Score
Livestock size
Metric
Female
5.8967**
94.27
Legend: * p < 0.005; ** p < 0.01; *** p < 0.001
23
Pearson’s Chi-squared test of association indicates that male-headed households have significantly higher
education level than female headed-households. Among those households whose head has completed at
least primary school, 85% are males against only 65% of females. Similarly, among those households whose
head has completed at least the secondary school, 11% are males, while only 6% are females.
In the first part of this section, I looked into the overall consumption expenditure distribution between male
and female-headed households, highlighting the relation between means of food and non-food
consumption. It is, now, interesting to shed light into the means distribution of food consumption shares
with respect to total food consumption. This would give an overview of the differences between male and
female-headed households in terms of food consumption patterns. Values are expressed in percentage and
they include both real food expenditure as well as the value of stocked food and donations.
All the sample t-tests, beside Vegetables and Fruits, are computed under the assumption of equal variance
between subgroups, since the Bartlett’s test of equal variance did not show evidence for rejection of the null
hypothesis (Cameron & Trivedi, 2010).
It is interesting to note that food shares distribution are not polarized around few food groups. In deed, all
food groups are represented and rather homogenous in size. They vary between 10% and 21% for male
headed households and between 9% and 24% for female headed households. This means that, in general,
households in the sample (both male and females headship) have access to a great variety of food and they
consume all type of food in approximately the same proportion.
Expenditures on vegetables and fruits (Veggie& Fruits) as well as sugar-based food and non-alcoholic
beverages (Sugar and non-) are significantly different between the two subsamples. While male-headed
households consume more on sugar-based food and sodas than female-headed households, the latter ones
consume more on vegetables and fruits.
TABLE 4 - DESCRIPTIVE STATISTICS OF FOOD GROUPS. MEANS (IN %) OF CONSUMPTION SHARES
Male
Expenditure
shares per
food groups Obs.
Mean (SD)
Cereals and
Starches
1400
Protein-rich
1400
Vegetables
and Fruits
Nuts, Fat
and Oil
Sugar and
nonAlcoholic
beverages
1400
1400
1400
1400
18.383
14.9725
16.736
14.329
21.359
12.947
9.987
8.909
10.228
8.3411
9.924
11.707
Female
obs.
227
227
227
227
227
227
Mean (SD)
19.321
15.50
15.643
14.329
23.634
14.817
10.609
9.815
9.139
8.390
10.754
11.685
Test Statistics:
(Ho: mean
P-values !=0 Equal
(men) - mean variance assumed
(women)
- 0.8705
0.3842
1.0660
0.287
- 2.1823**
0.0299
- 0.9614
0.3365
1.9226**
0.0547
- 0.9910
0.3218
24
Chapter 5
5 Results
This chapter contains the results of the analysis about the impact of gendered headship on food and nutrition
security. Following the livelihood approach, first, a description of the determinants of household’s
consumption (defined by consumption expenditures) is presented. It follows an analysis of the determinants
of total food consumption share (with respect to total consumption) and consumption shares disaggregated
for food groups. Finally, I will analyse the relation between gendered headship and nutritional food choices.
5.1 Determining the impact of gender on household consumption
“What is the effect of gendered headship on household consumption?”
As the table 2 indicates, 1617 observations used for this analysis. F-statistic is significant at less than 0.001%
indicating that, taken all together, variables explain a great deal of variance in the sample. Furthermore, R
squared is 0.255, which is a rather good value. Overall, those figures suggest that model and coefficients
explain variance rather well, therefore I can trust these findings. Almost all explanatory variables are
statistically significant. Most of them are significant at less than 0.001%, which indicates the power of the
explanatory variables is high.
TABLE 1 - DETERMINANTS OF PER CAPITA HOUSEHOLD CONSUMPTION
Per capita Total Consumption (lg)
Coefficients
Robust Std.
Error.
t-values
Explenatory Variables
Household size
-0.082***
0.011
-7.460
Gender of HH Head
0.205***
0.052
3.940
Age of HH Head
0.013
0.017
0.750
(square root) Age of HH Head
-0.105
0.237
-0.440
Primary education (at least)
0.173**
0.053
3.280
Secondary education (at least)
0.283***
0.055
5.150
Dependecy Ratio
-0.0051***
0.001
-5.580
Land size (lg)
0.193***
0.028
6.920
Assets owned
0.000***
0.000
4.680
Production equipments
-0.146***
0.025
-5.970
Cattle size
0.011**
0.003
3.220
Constant
13.382***
0.785
17.060
N
F
R squared
Note:
Legend: *p<0.005; **p<0.01;***p<0.001
1617
43.28***
0.2548
Huber-White sandwich estimator of Variance-Covariance Estimates (VCE)
25
Table 2 shows that gender is statistically significant from zero at 1% level. Since the variable was defined as
equal to 0 for male and to 1 for female headed-households, the table suggests that, being other variables
constant, female-headed households have a robust and positive per capita consumption effect when
compared to male-headed households. Results show that they are more associated with higher per capita
consumption by a factor of 20.5%, with respect to male-headed households. This result is surprising.
From descriptive statistics I found that, in my sample, female-headed households have lower level of assets
than the male counterpart does. This is the case, for all assets (and corresponding variables) in the livelihood
pentagon. Female-headed households have significantly smaller household size, smaller land and fewer
cattle. Moreover, their education level is more often a primary school level than higher. Therefore, I would
have expected that the variable G showed a significant and negative parameter, indicating that femaleheaded households are negatively associated with higher per capita consumption.
However, results show that this is not the case.
Table 2 shows also that the majority of the variables have a significant and positive effect on per capita
household consumption.
Figures show a significantly higher consumption effect among households whose head has completed at least
primary school education compared to the those ones whose head has a lower level. This effect is
significantly positive and stronger (17%). It is even stronger (28%) for household heads with secondary
education level.
This result sounds plausible since one can formulate the hypothesis that better educated households head
might be more productive. It might be the case since they are able to pick up innovation channelled through
extension services, for instance. They might be also able to trade a better price for their produce.
Durables (assets owned) owned have a positive per capita consumption effect. This variable estimates the
(median) value of the total consumption durables owned by the household. It indicates that, being equipped
with many and valuable durables is associated with higher household consumption. This result also seems
plausible since durables can be transformed into flow resources to be exchanged in the market.
Land size and cattle size have a positive effect on per capita consumption. Larger land size as well as higher
number of cattle (cows, in this case) imply higher inputs level. In return, higher input level might lead to
higher productivity and, therefore, larger per capita consumption. On the contrary, Agricultural equipment
is negatively correlated with per capita income. This result is surprising since, my hypothesis is that more
production equipment would enhance higher yields and consumption. However, this variable is an
aggregation of only a selected tool and production inputs, therefore it might be not very accurate.
In order to investigate whether gender plays a role in food and nutrition security, I need to explore what is
the effect of gender in food consumption and food consumption shares. This leads me to the next step of my
analysis.
5.2 Determining the impact of gender on food consumption patterns
“What is the effect of gendered headship on household food consumption pattern?”
The following paragraph encompasses results from multivariate regressions analysis on food consumption
expenditures. Usually, when expenditure patterns are analysed is useful having estimates of both the overall
26
food expenditure and of expenditure shares of each food groups in order to make comparisons. Table … (3)
shows STATA output of seven OLS regressions with Huber-White sandwich estimator of the VarianceCovariance Estimates matrix, indicating that the standard errors are robust.
For each models, 1626 observations have been included in the analysis. The F statistics (F-stat) were all
significant at 5% and R-squared adjusted (R² ad.) ranged between 0.03 (Alcoholic beverages) and 0.33 (Total
Food Expenditure share). Moreover, Root Mean Squared Errors (RMSE) ranged between 8.2 (Sugar-based
food and Non-alcoholic Beverages) and 18.1 (Total Food Expenditure Share).
The majority of OLS models report insignificant coefficients for gender of the household head. Only Sugar
and Non-alcoholic beverages and Vegetables and Fruits variables shares show significance with respect to
gender of the household head. Since the gender variable is constructed such as it is 1 for female head and 0
otherwise, results show that female headed households are significantly associated with a decrease in sugar
and non-alcoholic drinks expenditure of 1.25%, when compared with male headed households. They are also
associated with increase in Vegetables and Fruits consumption expenditure by 2.19%. However, those results
are only significant at 0.05%, therefore they have a limited explanatory power.
As the table 3 indicates, Total Food Consumption Share appears to be significantly and negatively correlated
with Total Consumption (ln) (estimates -14.08), meaning that when total consumption expenditure increases
by 10%, food share decreases by 1.40. This appears to be consistent with theory and the Engel law, since the
proportion of consumption dedicated to food tends to diminish for larger consumption levels, here identified
by the proxy total expenditure (Varian & Repcheck, 2010).
Total Food Consumption share is also negatively but significantly associate with the two dummy variables
indicating the household head education level namely primary/higher completed level and secondary/higher
completed level (-4.31 and -4.05). When compared to household head with few years or no years of studies,
households whose head is better educated tend to consume more, making consumption for food becoming
less important. Signs are negative.
Total Food Consumption share is positively associated with some household characteristics, such as
household composition and size. While household size is significant at 0.1%, dependency ratio is significant
even at 1%. Results indicate that for each additional household member, food consumption share decreases
of 1.99% and that when dependency ratio increases of 1 unit, food consumption share increases of 0.07 %.
When the individual food groups are analysed, only two factors seems to be significantly correlated with food
group shares: total food consumption and household size.
Provided that in almost all food groups, total food consumption is significant at 0.1%, the OLS estimates
strongly suggest that consumption in cereals, proteins-rich food and alcoholic beverages increases when
food consumption increases, indicating that those commodities are normal goods (Varian & Repcheck, 2010).
Particularly, when per capita food consumption increases of 10%, consumption share in cereals goes up by
0.6%, in protein by 0.8% and alcoholic consumption goes up by 0.2%. It is interesting to note that
consumption in protein-rich food increases more than consumption in cereals, therefore those two food
groups are considered substitutes (Varian & Repcheck, 2010). This means also that when additional resources
are available and consumption can be expanded, households tend to increase consumption of higher quality
food, such as, protein-rich food.
On the contrary, vegetables and fruits, nuts and fats, as well as non-alcohols show significantly negative
coefficients. When households consume +10% of food, consumption of those commodities is reduced by
0.5%, 0.1% and 0.2% respectively. Such a result shows that vegetable and fruits as well as nuts and fats,
together with non-alcohols are inferior goods (Varian & Repcheck, 2010). However, results also indicate that
27
effect of nuts and fats shares are very limited. On the contrary, vegetables and fruits as well as non-alcoholic
are rather robust being significant at less than 0.1% and 1%, respectively.
To sum up, these findings suggest that when consumption increases, households tend to consume more on
cereals, protein and alcohol, and less on vegetables and fruits, nuts and oils as well as sugars and nonalcoholic beverages. It is interesting to highlight that the important properties contained in vegetables and
fruits as well as in nuts seems not to be a driver of food consumption behaviour, neither for female-headed
households nor for male-headed households, provided that consumption expenditures in those commodities
decreases when food consumption increases. Whilst, alcohol seems to be one of the driver since it is very
demanded: as food consumption levels spin up, so it does alcohol consumption share.
Household size is significant and negative in 4 out of 6 food groups, namely cereals and starches, proteins,
vegetables and fruits as well as non-alcoholic beverages. Results indicate that for any additional member,
households tend to reduce consumption expenditure by 0.6% for cereals, by 0.5% for protein-rich food, by
0.5% for vegetables and fruit and by 0.4% for alcohols.
Taken all together, results about food consumption pattern show that household composition, size and
education of the household head, play a role in determining the proportion of consumption dedicated to
food (both in terms of total consumption expenditure and per food group shares).
It is interesting to compare results about food groups shares with the OLS figure for total food consumption
share. While I found that the consumption share dedicated to food decreases when overall consumption
increases, indicating that richer households spends less on food (than on other products), nonetheless, this
share is dedicated to improve the variety of the diet. As a result, their nutrition is better off compared to
households with lower expenditure patterns. This feature is confirmed also when one look at the
consumption of food groups. I found that when additional resources are available, households tend to
consume high quality food such as protein-rich food.
28
TABLE 2 - OLS ESTIMATES OF FOOD GROUPS SHARES
OLS - robust
Cereal and
other
starches
Proteinrich
Vegetables
and Fruits
Nuts, Fats,
Oils
Sugar and nonalcoholic
beverages
Alcoholic
beverages
5.57***
7.6***
-4.66***
-1.01*
-1.52**
2.5***
-0.134
(1.48)
1.01*
(0.45)
-14.62*
(6.32)
0.067**
(0.023)
-1.99***
(0.258)
(0.644)
1.26
(1.14)
0.325
(0.414)
-4.84
(5.67)
-0.04
(0.018)
-0.633**
(0.206)
(0.659)
-1.18
(1.02)
-0.279
(0.354)
3.83
(4.91)
-0.0061
('0.0158)
-0.52**
(0.171)
(0.788)
2.19*
(1.08)
-0.06
(0.319)
1.54
(4.41)
0.016
(0.0169)
-0.54**
(0.168)
(0.392)
0.366
(0.715)
0.521
(0.327)
-7.21
(4.46)
0.014
(0.0109)
-0.199
(0.117)
(0.595)
-1.25*
(0.629)
0.198
(0.247)
-3.78
(3.59)
-0.017
(0.0102)
-0.378***
(0.123)
(0.534)
0.0127
(0.881)
-0.497
(0.323)
8.2
(4.44)
-0.003
(0.0143)
-0.285
(0.153)
-4.31**
(1.33)
-4.05**
(1.53)
321***
(23.5)
0.455
(1.16)
3.16**
(1.16)
-38.5
(20.9)
2.16*
(1.06)
0.683
(1.05)
-97.5***
(18.4)
0.77
(1.06)
-1.13
(0.846)
75.7***
(17.6)
-0.876
(0.72)
-0.736
(0.621)
48.6**
(15.6)
0.601
(0.604)
1.06
(0.584)
48.3**
(15)
0.646
(0.967)
-0.159
(0.889)
-53***
(15)
1626
2.61
0.0198
0.015
8.98
1626
6.1
0.0427
0.038
8.2
1626
7.12
0.0337
0.0289
11.5
Dependent Variables
Total Food
Share
Per capita total Consumption
(ln)
-14.078***
(0.760)
Per capita Total Food
Consumption (ln)
Gender of HH head
Age of HH head
Age of HH head (squared)
Dependency ratio
HH size
HH head education level
a t l ea s t pri ma ry compl eted
a t l ea s t s econda ry compl eted
constant
N
1626
1626
1626
1626
F
88.5
13.6
24.8
7.01
R2
0.339
0.0724
0.134
0.0619
Adjusted R
0.336
0.0678
0.13
0.0573
Root Mean Square Errors
18.1
14.5
13.4
12.9
Legend: * p<0.05; ** p<0.01; ***p<0.001
Huber-White sandwich estimator of Variance-Covariance Estimates (VCE) matrix used
Analysing household consumption and gender, in my first model, results indicated that gender plays a role in
determining per capita consumption. The gender variable is significant and positive. Particularly, figures
suggested that, ceteris paribus, female-headed households show larger per capita consumption levels than
their male counterparts.
However, such a significance disappears when it comes to analyse food consumption, I n my second model.
In this case, gender was not significant. This indicates that female-headed households do not show a different
food consumption behaviour than male-headed households. Moreover, a significant and robust variable is
per capita total consumption expenditure. Therefore, these results suggest that, no matter whether female
or male headed, households’ choices are driven by assets accumulation being proxied by consumption
expenditure.
This result seems to confirm the hypothesis that asset accumulation remain the major determinant of
households’ livelihood.
It is interesting to investigate whether such a result is confirmed when food choices are analysed. I will do so
by investigating whether gender is a driver in dietary diversity.
29
5.3 Determining the impact of gender on dietary diversity
“What is the effect of gendered headship on household diet diversity?”
Table 5 shows results of the analysis on Women Dietary Diversity Score, used as proxy for household diet
diversity, based on the ordered probit model described in equation 9. The table is divided in two parts:
coefficients are shown on the right side, while margins and z-values are reported on the right side. The
marginal values indicate the percent change in likelihood of households having a low, medium and high diet,
when the corresponding dependent variables increase of 1 unit, while others are kept constant. Marginal
values are average marginal effects.
A thousand and twenty-six observations were analysed. The Likelihood Ratios, which indicates the
explanatory power of the model, is reported and it is significant at 0.01%. Pseudo- R² is slightly low and it is
0.03. However, the Pseudo- R² is not as powerful as R² that it is used in OLS, therefore a better source of
goodness of fit is the percent of correctly predicted probabilities (Agresti, 2002).
As the coefficient table (left side) shows, only a few variables are positive and significant: namely total
household consumption expenditure (0.288), household size (0.046) and education of household head
(0.318). This means that the variety of diet gets better (going from low, to medium to high variety) for
households with higher overall consumption expenditure, larger household size and whose head has
completed at least the secondary school.
The right side table shows margins of the ordered probit model. It indicates that one unit increase of
consumption expenditure is associated with women being 9.9% less likely to have a low diversity in diet and
being 10 % more likely to have high dietary diversity. In other words, households with higher consumption
expenditure levels have larger chances to have greater variety in their diet.
Moreover, household head education is also important in my sample. However, while low level of education
of the household head, indicated by the variable “primary completed”, seems to not play a role in the
likelihood of having a diverse diet, high level of education results to be significant (and positively associated).
When the head of the household has obtained the secondary level of education, women are 11% less likely
to be in the low category, while they are 10.4% more likely to be in the high category. In other words, better
educated households head are more likely to have greater diversity in household dietary patterns. Table 6
shows also that Total Household expenditure is not significant in the case of the medium WDDS.
In this last step, gender is not significant in explaining WDDS. Therefore, gender is not a driver in household
dietary diversity and it plays no role in (good nutritional) food choices. This result seems to confirm what
found already in the second model.
30
TABLE 5 - COEFFICIENTS AND MARGINS OF ORDERED PROBIT MODEL
Probit Model coefficients
Variables
coefficients
Robust
standard errors
0.288***
0.037
Gender of HH head
-0.178
0.087
Age of HH head
-0.044
0.029
Age of HH head (squared)
0.591
0.410
Dependency ratio
0.002
0.001
HH size
0.046*
0.015
HH head education level at least primary completed
0.056
0.083
0.318**
0.104
Per Capita Total Consumption (ln)
at least secondary completed
cut-off point 1
cut-off point 2
Margins of Probit Model
Low (SD)
z-values
-0.099***
(0.012)
0.052
(0.029)
0.013
(0.010)
-0.175
(0.14)
0.000
(0.000)
0.016*
(0.005)
-0.016
(0.028)
-0.11**
(0.035)
-7.92
1.77
1.32
-1.26
-1.33
0.005
-0.56
-3.08
Medium
(SD)
0.005
(0.003)
-0.002
(0.002)
0.000
(0.000)
0.008
(0.009)
0.000
(0.000)
-0.000
(0.000)
0.000
(0.001)
0.005
(0.004)
z-values
High (SD)
z-values
1.39
0.093***
(0.012)
-0.049
(0.028)
-0.012
(0.010)
0.166
(0.132)
0.000
(0.000)
0.015*
(0.005)
0.015
(0.027)
0.104**
(0.040)
7.77
-1.08
-0.95
0.93
0.95
-0.54
0.52
1.3
-1.77
-1.32
1.26
1.34
-0.59
0.56
3.05
5.466
6.698
N
1626
Likelihood Ratio chi2
100.63***
prob > chi2
0.000
Pseudo-R2
0.0305
Legend: * p<0.05; **p<0.01; *** p<0.001
Notes:
Low: 1+2 food groups (29% of obs.); Medium: 3 food groups (45%) ; High: 4+5+6+7 food groups (26%)
N indicates number of observation;
31
Chapter 6
6 Discussion
As seen in model 1, gender had significant and strong effect in determining per capita consumption, being
assets constant. However, such an effect was not found when per capita food consumption was analysed in
model 2. Studying nutritional livelihood outcomes via the ordered probit model (3), I found that gender is
not a driver in good nutritional food choices.
On the contrary, also in this third model, the level of per capita consumption, as a proxy for assets
accumulation and household income, resulted to be the real driver of food consumption patterns and
nutrition.
Therefore, the advantage of being a female-headed households (that was showed in the first model) when
assets were analysed, did not compensate for the lack of assets as showed in the second and third model.
Asset accumulation kept being the major determinant of in the relation between gendered headship and
food and nutrition security throughout the whole analysis. Thus, my study shows that, since female-headed
household have lower level of assets, the range of their livelihood strategies is restricted at the beginning of
the decision chain, from income accumulation to nutritional food choices, regardless the assumed higher
preferences for food.
I expected the (negative) assets constraints effect to be larger than the (positive) effect of food preference,
provided that, descriptive statistics showed a lower level of asset among female-headed households.
However, the results of model 1, showed a different story. A number of hypothesis can be formulated to
explain such an unexpected result:
Female headed households have higher consumption because they mange to mobilize their social capital to
get access to different resources such as food. I did not explicitly account for social capital in my first model,
where I analysed the relation between assets and per capita consumption. For instance, female-headed
households might relay more often on food donated by local charities (or NGOs) and local supportive groups
such as relatives or friends.
Another hypothesis explaining the higher per capita consumption can be related to a methodological issue.
Per capita consumption has been calculated as the overall consumption expenditure divided by household
size. Since, in my sample, female-headed households have fewer members, per capita consumption might
be overestimated, since, in calculating the per capita consumption, the denominator in the female-headed
households is a smaller number than the denominator used to compute the per capita consumption of maleheaded households. If this is the case, the gender variable might result negative associated with higher per
capita consumption level. As a result, the relation between gendered headship and overall consumption
would follow the hypothesis stating that lack of asset leads to worse livelihood strategies and smaller income,
consumption as well as food insecurity.
Other issues might be influence my results are linked to concept such as household as a unit of analysis,
household headship, the type of assets include in model 1 as proxy of income level and, lastly, bias in
consumption level reported. Those are discussed below.
32
A critical point is related to the choice of household as a unit of analysis. Many pointed out that power is
unequally distributed among household members with important implication for resource allocation and
household’s welfare (Chant et al., 2007). This occurs in the relation between women and men as well as
children and adult. (Doss, 2013). In order to detect welfare outcomes, a large set of empirical research study
the differences in power and preferences to determine resource allocations among household’s members.
Analysing the determinants of bargaining power it could has been given different consumption results
providing more accurate evidence in the relation between gender and food and nutrition security.
Connected to this, it is also the issue of the correct definition of household headship. It is assumed that the
when two spouses are present, the husband is the head of the household and he is the person who has the
highest power in the decision-making process. However, studies have showed that even in male-headed
households wives take responsibilities activities and have authority over a number of issues such as when
they earn income from off-farm activities (Kabeer, 2003). Attempting to disentangle the power relation
among members with power over different issues, researchers have constructed alternative definitions of
household head such as “working head” or “cash head” (Quisumbing et al., 2001) (Lloyd & Gage-Brandon,
1991). Correctly identifying the most powerful member in the household have implication over welfare
outcomes estimation, among other food and nutrition security.
Moreover, other studies have suggest the need to investigate more why women are indicated as head in
female-headed households. A distinction between de fact or the jure households rose in recent years. In de
facto female headed households, the male adult head is absent for long period in the rural area. A typical
example is the case where the husband migrate for work and send remittances back home. In this kind of
households, the husband might still have an influence in basic decision-making process and contribute to the
household income. On the contrary, de jure female-headed households are those where women are
considered head legally and socially. Widows, unmarried, divorced or separated are usually head of such
households. In this case, households are realistically the solely decision maker actor. Being able to detect
such a distinction leads to a more accurate analysis and to different results. For instance, de facto femaleheaded households might being less affected by locally available assets because they might rely on
remittances provided by the absent adult. Whilst, de jure female-headed households might be highly affected
by the lack of assets and thus, poorer.
Another point of discussion is the type of assets that have been use to define the five basic capital
endowment in model 1. It can be assumed that, in addition to the asset included in the model, female-headed
households might mobilize a range of other assets whose returns (unobserved) are higher than those of the
asset used for model 1. This could explain the higher effect on per capita consumption found on femaleheaded household.
An ultimate hypothesis is related to possible biased in consumption reported by male-headed households.
In this household, the wife was asked to answer consumption questions. I assumed that each member
collaborate towards the same household utility level and reach a Pareto efficiency, as expressed in a
collaborative model of household decisions. In addition, it is important to recall that half of the respondent
households were part of the “treatment group” that received assistance by the Dutch ngo SNV. My research
is linked to this project since the survey was originally designed to assess the efficiency of SNV in the region
of Mbeya. Provided this setting, it might be assumed that the wife in male-headed households has
systematically reported lower level of consumption with the hope that this would make the household
appear poorer than it actually is and it would result in an advantage for the household.
33
6.1 IV approach
In order to investigates more on the relation between gender and food consumption expenditure share, I
run a two-stages-Least-Squares Instrumental Variable (IV) regression. I indicated cattle size (Cattle), land size
(land),land ownership of pagna knife and presence of a cemented floor in the household’s dwelling as
exogenous instruments. I used some demographic characteristic as endogenous instruments. Those
comprise variables such as, gender of the household head (G), level of education (Edu1 and Edu2),
Dependency ratio (Dep), household size (Size) and age (linear and non-linear relation) of the household head
(Age and AgeSq).
The IV estimates were run in STATA, using ivreg2 module with robust option for standard errors (Baum,
2006). Estimates were efficient for assumed homoscedasticity, while statistics are robust to
heteroscedasticity.
For this analysis, 1601 observations were used. F-statistic of the overall fit of the models were significant
across all food groups and RMSE ranged between 8.61 of the estimates for Sugar and Non-Alcoholic
expenditure share and 18.5 for the estimates of Total Food expenditure share.
Moreover, the first stage estimations (table 6) show that the explanatory power of the instruments are strong
enough. All the F-stat of the joint significance of the instruments are larger than 10. The under-identification
test indicates that all the models are identified since the Kleibergen-Paap rK Lagrange Multiplier (LM)
statistics shows that we do reject the null hypothesis of under-identification. This indicates that the excluded
instruments are correctly correlated with included instrument or endogenous regressors. This is confirmed
by the first stage pair-wise correlation estimates presented in tab… According to the table, estimates of the
excluded instruments are significant at least at 5%.
TABLE 6 - FIRST STAGE IV REGRESSION OUTPUT
Total Expenditure (Ln)
Total Food Expenditure
(ln)
.17***
.0735***
livestock size (cattle)
.00745**
.00576*
panga knife
.193***
.188***
Cemented floor
.405***
.296***
Gender HH head
.139**
0.0613
Age of HH head
-0.0268
-0.0155
Age of HH head
(squared)
0.394
0.215
Variables
Land size (ln)
Dependency ratio
-.00383***
-.00154*
HH size
at least primary
school completed
at least secondary
school completed
N. of observations
Root Mean Square
Erros
.107***
.0942***
.155**
0.0516
.312***
.197***
legend: *
1601
1601
0.687
0.597
p<.05; ** p<.01; ***
p<.001
However, when testing the validity of the instruments, the Sargan-Hansen test for over-identification
indicates that we do not reject the null hypothesis of endogeneity for the majority of the models. Thus, for
those equations, test suggests that the instruments are correlated with the error terms, and that they are
34
not exogenous. There must be an omitted factor that is correlated with both consumption expenditure share
(per food group) and with total consumption expenditure. This leads me to the conclusion that, I cannot trust
the validity of the IV coefficient since it is not possible to correctly isolate the effect of the instrument.
Therefore, I stopped the analysis and I relied only on the previous models for my conclusions.
Tables related to the Validity and Orthogonality tests are provided in Appendix
35
Chapter 7
7 Conclusions
This study aimed to provide an understanding of the gender effect in household food and nutrition security.
In order to achieve my goal, I followed the Sustainable Livelihood Framework, where investigating the five
assets’ pentagon is the starting point of the analysis. Therefore, a description of the determinants of per
capita consumption was presented. Descriptive statistics have showed that, compared to male-headed
households, female-headed households have smaller total consumption expenditure and smaller assets level
(land, cattle, education, household size). However, when female-headed households were compared with
their male counterparts with the same level of assets, they were associated with higher per capita
consumption.
It followed an analysis of the determinants of food consumption share and consumption share disaggregated
for food group, by which I gave sights on whether gender plays a role in food consumption.
Results showed that the gender factor, that was significant and strong in the first model, had no effect when
it came on analysing food consumption. Instead of gender, Total consumption expenditure resulted to be
significant. This indicated that female-headed households did not show a different food consumption
behaviour than male-headed households. Yet household’s choices were driven by availability of assets,
especially financial ones, regardless of the gender factor.
Lastly, I took a step further in the analysis and I explored whether or not gender bear an effect in what
determine food choices and the relative nutritional outcomes. I investigated which variables influence the
probability of household consuming an adequate dietary pattern, throughout the WDDS. In this last step,
gender seemed not to play a role in dietary adequacy.
Therefore, the advantage of being a female-headed households (that was showed in the first model) when
assets were analysed, did not compensate for the lack of assets as showed in the second and third model.
My study seems to suggest that asset accumulation might be determined by the gender factor itself, since
female-headed households tend to be assets constrained probably also because they are lead by a
(constrained) woman. As descriptive statistics showed, female-headed households in my sample, had lower
land, households and cattle size and they were also less educated. However, this relation keeps being very
unclear and my analysis has not comprised enough variables and level of analysis to systematically address
this issue. An attempt to do so was provided by the Instrumental Variable analysis briefly addressed in the
next paragraph. However, since the selected exogenous instruments did not pass the validity tests, no
reliable conclusion can be drawn.
Additional research should be carried on to address clearly direction of the causal relation in the asset-gender
issue in poor farming households in developing countries.
36
yes
3.007
9.522
Alcoholic beverages
WDDS
0.000
1.843
Sugar and non-alcoholic
beverages
183.077
4.104
Nuts, Fats, Oils
0.000
7.189
124.179
Vegetables and Fruits
10.946
17.387
0.000
Protein-rich
chi2 p val
31.358
183.077
Kleibergen-Paap rk
LM
0.023
0.391
0.606
0.250
0.066
0.001
0.000
0.012
Are the models identified?
Over-identification test
Are excluded instruments
correctly correlated with the
endogenous regressor
Sargan-Hansen J-stat
chi2 p-val
(relevant)?
Cereal and other starches
Total Food Share
Models
Under-identification test
IV - robust
no
yes
yes
yes
yes
no
no
no
no
yes
yes
yes
yes
no
no
no
instruments are
uncorrelated with
Are Instruments errors of main equ.?
valid? Are they excluded instrument
exogenous? are correclty
excluded from the
equation?
Appendix
TABLE 7- UNDER-IDENTIFICATION TEST AND OVER-IDENTIFICATION TEST
37
TABLE 8 - CRAGG-DONALS WALD F-STAT AND CRITICAL VALUES
Strenght of Instruments
Cragg-Donald Wald F-stat
Total Expenditure
67.556
Total Food Expenditure
39.619
critical values Stock&Yogo
maximal IV relative bias
maximal IV size
5% = 16.85
10% = 10.27
20% = 6.71
30% = 5.34
10% = 24.58
15% = 13.96
20% = 10.26
25% = 8.31
38
References list
(FAO) Food and Agriculture Organization of the United Nations. (2002). Food balance sheet and food
consumption survey: a comparison of methodologies and results (No. 7). Retrieved from
http://www.fao.org/elearning/course/FA/en/pdf/7_FBS_FCS.pdf
Agresti, A. (2002). Catagorical Data Analysis. doi:10.1007/s007690000247
Baum, C. F. (2006). An introduction to modern Econometrics using Stata. (Stata Corporation, Ed.). STATA
PRESS.
Bertelli, O., & Macours, K. (2014). Food Security and Agriculture in Developing Countries: Measurement and
hypotheses for impact evaluations. FOOD SECURE Working Paper, (21).
Bhutta, Z. A., Das, J. K., Rizvi, A., Gaffey, M. F., Walker, N., Horton, S., … Black, R. E. (2013). Evidence-based
interventions for improvement of maternal and child nutrition: What can be done and at what cost?
The Lancet. doi:10.1016/S0140-6736(13)60996-4
Black, R. E., Alderman, H., Bhutta, Z. A., Gillespie, S., Haddad, L., Horton, S., … Webb, P. (2013). Maternal
and child nutrition: Building momentum for impact. The Lancet. doi:10.1016/S0140-6736(13)60988-5
Black, R. E., Victora, C. G., Walker, S. P., Bhutta, Z. A., Christian, P., De Onis, M., … Uauy, R. (2013). Maternal
and child undernutrition and overweight in low-income and middle-income countries. The Lancet.
doi:10.1016/S0140-6736(13)60937-X
Boserup, E. (1970). Women’s role in Economic Development. (Earthscan, Ed.). London.
Cameron Colin, A., & Pravin Trivedi, K. (2009). Microeconometrics using STATA. (S. Press, Ed.) (Stata Corp).
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata (Vol. 2). Stata Press College Station,
TX.
Chambers, R., & Conway, G. (1992). Sustainable Rural Livelihoods: practical concept for the 21st century.
London: Institute for Development Studies.
Chant, S. H. (2003). New contributions to the analysis of poverty: methodological and conceptual challenges
to understanding poverty from a gender perspective (Vol. 47). United Nations Publications.
Chant, S. H. (2007). Gender, generation and poverty: exploring the feminisation of poverty in Africa, Asia
and Latin America. Edward Elgar Publishing.
Cochrane, N., & Souza, A. D. (2015). Measuring Access to Food in Tanzania : A Food Basket Approach, (135).
Retrieved from http://www.ers.usda.gov/media/1784075/eib135.pdf
Dancause, K. N., Akol, H. A., & Gray, S. J. (2010). Beer is the cattle of women: Sorghum beer
commercialization and dietary intake of agropastoral families in Karamoja, Uganda. Social Science and
Medicine, 70(8), 1123–1130. doi:10.1016/j.socscimed.2009.12.008
Department for International Development. (1999a). Sustainable Livelihoods Guidance Sheets. Framework.
Ids Discussion Paper. doi:10.1002/smj
Department for International Development. (1999b). Sustainable Livelihoods Guidance Sheets. Introduction.
Ids Discussion Paper.
Doss, C. (2006). The Effects of Intrahousehold Property Ownership on Expenditure Patterns in Ghana.
Journal of African Economies, 15(1), 149–180. doi:10.1093/jae/eji025
Doss, C. (2013). Intrahousehold bargaining and resource allocation in developing countries. World Bank
Research Observer, 28(1), 52–78. doi:10.1093/wbro/lkt001
39
Ellis, A., Blackden, M., Cutura, J., MacCulloch, F., & Seebens, H. (2007). Gender and Economic Growth in
Tanzania: Creating Opportunities for Women. Retrieved from
http://search.ebscohost.com/login.aspx?direct=true&db=ecn&AN=0978889&site=bsi-live
Ellis, F., & Mdoe, N. (2003). Livelihoods and rural poverty reduction in Tanzania. World Development, 31(8),
1367–1384. doi:10.1016/S0305-750X(03)00100-1
FAO. (2010). THE STATE OF FOOD AND AGRICULTURE - WOMEN IN AGRICULTURE Closing the gender gap
for development. Food and Agriculture Organization.
FAO Nutrition and Consumer Protection Division. (2008). Guidelines for measuring household and individual
dietary diversity.
FAO; IFAD and WFP. (2014). The State of Food Insecurity in the World 2014. Strengthening the enabling
environment for food security and nutrition.
FAOSTAT. (2014). United Republic of Tanzania - country profile - January, 24 2014. Retrieved from
http://faostat.fao.org/CountryProfiles/Country_Profile/Direct.aspx?lang=en&area=215
FAOSTAT. (2016). Food Balance Sheets. Retrieved from http://faostat3.fao.org/compare/E
Hadley, C., Lindstrom, D., Tessema, F., & Belachew, T. (2008). Gender bias in the food insecurity experience
of Ethiopian adolescents. Social Science and Medicine, 66, 427–438.
doi:10.1016/j.socscimed.2007.08.025
Horrell, S., & Krishnan, P. (2007). Poverty and Productivity in Female-Headed Households in Zimbabwe. The
Journal of Develpment Studies, 43(August, 2007), 1351 – 1380. doi:10.1080/00220380701611477
Idalinya, J., Bauer;, S., & Siddig;, K. (1987). Determinants of farm household food expenditure and
implications for food security and nutrition in rural Kenya. One Thousand Plateaus, 3–25. Retrieved
from http://hollis.harvard.edu/?itemid=
Kabeer, N. (2003). Gender Mainstreaming in Poverty Eradication and the Millennium Development Goals: A
Handbook for Policy-makers and Other Stakeholders. Retrieved from
http://books.google.com/books?hl=en&lr=&id=SAHGtZEgka0C&pgis=1
Kassie, M., Ndiritu, S. W., & Stage, J. (2014). What Determines Gender Inequality in Household Food
Security in Kenya? Application of Exogenous Switching Treatment Regression. World Development, 56,
153–171. doi:10.1016/j.worlddev.2013.10.025
Keding, G. B., Msuya, J. M., Maass, B. L., & Krawinkel, M. B. (2012). Relating dietary diversity and food
variety scores to vegetable production and socio-economic status of women in rural Tanzania. Food
Security, 4, 129–140. doi:10.1007/s12571-011-0163-y
Kurz, K. M., & Johnson-welch, C. (2000). Enhancing Nutrition Results : The Case for a Women ’ s Resources
Approach, (March).
Lanjouw, Jean., Lanjouw, P. (2001). The rural nonfarm sector: issues and evidence from developing
countries. Agricultural Economics, 26, 1–23. doi:10.1111/j.1574-0862.2001.tb00051.x
Lloyd, C. B., & Gage-Brandon, A. J. (1991). Women’s role in maintaining households: Poverty and gender
inequality in Ghana.
Lokosang, L. B., Ramroop, S., & Hendriks, S. L. (2011). Establishing a robust technique for monitoring and
early warning of food insecurity in post-conflict South Sudan using ordinal logistic regression. Agrekon,
50(4), 101–130. doi:10.1080/03031853.2011.617902
Mason, R., Ndlovu, P., Parkins, J. R., & Luckert, M. K. (2014). Determinants of food security in Tanzania:
gendered dimensions of household headship and control of resources. Agriculture and Human Values,
539–549. doi:10.1007/s10460-014-9568-5
40
Mbululo, Y., & Nyihirani, F. (2012). Climate Characteristics over Southern Highlands Tanzania. Atmospheric
and Climate Sciences, 02(04), 454–463. doi:10.4236/acs.2012.24039
Ndobo, F., & Sekhampu, T. J. (2013). Determinants of Vulnerability to Food Insecurity in a South African
Township: A Gender Analysis. Mediterranean Journal of Social Sciences, 4(14), 311–317.
doi:10.5901/mjss.2013.v4n14p311
Ogato, G. S., Boon, E. K., & Subramani, J. (2009). Improving Access to Productive Resources and Agricultural
Services through Gender Empowerment: A Case Study of Three Rural Communities in Ambo District,
Ethiopia. Journal of Human Ecology, 27(2), 85–100. Retrieved from
http://search.ebscohost.com/login.aspx?direct=true&db=ioh&AN=3970573&site=ehost-live
Peerlings, J. H. M. (2014). Syllabus of Rural Economic Analysis - Agricultural Economics and Rural Policy
Group. Wageningen University and Research.
Quisumbing, A. R. (2003). Household decisions, gender, and development: a synthesis of recent research.
International Food Policy Research Institute.
Quisumbing, A. R., Haddad, L., & Peña, C. (2001). Are women overrepresented among the poor? An analysis
of poverty in 10 developing countries. Journal of Development Economics, 66(1), 225–269.
doi:10.1016/S0304-3878(01)00152-3
Savy, M., Martin-Prével, Y., Traissac, P., & Delpeuch, F. (2007). Measuring dietary diversity in rural Burkina
Faso: comparison of a 1-day and a 3-day dietary recall. Public Health Nutrition, 10(01), 71–78.
doi:10.1017/S1368980007219627
Scoones, I. (2009). Livelihoods perspectives and rural development. Journal of Peasant Studies, 36(1), 171–
196. doi:10.1080/03066150902820503
Sraboni, E., Malapit, H. J., Quisumbing, A. R., & Ahmed, A. U. (2014). Women’s empowerment in
agriculture: What role for food security in Bangladesh? World Development, 61, 11–52.
doi:10.1016/j.worlddev.2014.03.025
The World Bank. (2011). Reproductive Health at a Glance: Tanzania. Retrieved from
http://siteresources.worldbank.org/INTPRH/Resources/3763741303736328719/Tanzania3212web.pdf
Varian, H. R., & Repcheck, J. (2010). Intermediate microeconomics: a modern approach (Vol. 6). WW Norton
& Company New York, NY.
World Bank. (2015). World Development Indicators. Retrieved from
http://databank.worldbank.org/data/reports.aspx?source=2&country=TZA&series=&period=
41